SoroushVahidi/scidocs huggingface dataset:
Semantic Parsing with Syntax- and Table-Aware SQL Generation
(2018)

We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query wordby-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest questionSQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.
Deep Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Imagery
(2017)

A semantic segmentation algorithm must assign a label to every pixel in an image. Recently, semantic segmentation of RGB imagery has advanced significantly due to deep learning. Because creating datasets for semantic segmentation is laborious, these datasets tend to be significantly smaller than object recognition datasets. This makes it difficult to directly train a deep neural network for semantic segmentation, because it will be prone to overfitting. To cope with this, deep learning models typically use convolutional neural networks pre-trained on large-scale image classification datasets, which are then fine-tuned for semantic segmentation. For non-RGB imagery, this is currently not possible because large-scale labeled non-RGB datasets do not exist. In this paper, we developed two deep neural networks for semantic segmentation of multispectral remote sensing imagery. Prior to training on the target dataset, we initialize the networks with large amounts of synthetic multispectral imagery. We show that this significantly improves results on real-world remote sensing imagery, and we establish a new state-of-the-art result on the challenging Hamlin Beach State Park Dataset.
ATRIUM: A Radar Target Simulator for Complex Traffic Scenarios
(2018)

Future autonomous automobiles will require extremely reliable radar sensors to guarantee the safety of road users. This requires test environments to verify that automotive radars work properly even in complex traffic situations. Radar target simulators can generate virtual scattering centers in front of the radar sensor and are therefore a promising verification technique. However, currently available systems only offer a very basic approach to manipulating scattering centers. With the workflow presented in this publication it becomes possible to decompose high-level descriptions of traffic scenarios into parameters required for radar target simulation. The workflow can therefore be used to generate realistic traffic scenarios and thus increases the reliability of automotive radar sensor tests.
SentiWordNet for Indian Languages
(2010)

The discipline where sentiment/ opinion/ emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found in sentiment lexicon literature deal mostly with English texts. In this article, we propose multiple computational techniques like, WordNet based, dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages. Currently, SentiWordNet(s) are being developed for three Indian languages: Bengali, Hindi and Telugu. An online intuitive game has been developed to create and validate the developed SentiWordNet(s) by involving Internet population. A number of automatic, semi-automatic and manual validations and evaluation methodologies have been adopted to measure the coverage and credibility of the developed SentiWordNet(s).
Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems
(2012)

An efficient optimization method called ‘Teaching–Learning-Based Optimization (TLBO)’ is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods. 2011 Elsevier Inc. All rights reserved.
The Evolution of Accuracy and Bias in Social Judgment
(2005)

Humans are an intensely social species and therefore it is essential for our interpersonal judgments to be valid enough to help us to avoid enemies, form useful alliances and find suitable mates; flawed judgments can literally be fatal. An evolutionary perspective implies that humans ought to have developed sufficient skills at solving problems of interpersonal judgment, including gauging the personalities of others, to be useful for the basic tasks of survival and reproduction. Yet, the view to be derived from the large and influential bias-and-error literature of social psychology is decidedly different—the social mind seems riddled with fundamental design flaws. We will argue in this paper that flawed design is probably the least plausible explanation for the existence of so many errors. We present an evolutionarily-based taxonomy of known bias effects that distinguishes between biases that are trivial or even artifactual and lead virtually nowhere, and those that have interesting implications and deserve further study. Finally, we present an evolutionary perspective that suggests that the ubiquity, automaticity, and success of interpersonal judgment, among other considerations, presents the possibility of a universal Personality Judgment Instinct. Archeological evidence and behavioral patterns observed in extant hunter-gatherer groups indicate that the human species has been intensely social for a long time (e.g., Chagnon, 1983, Tooby & Devore, 1987). Human offspring have a remarkably extended period of juvenile dependency, which both requires and provides the skills for surviving in a complex social world (Hrdy, 1999). Humans evolved language and universal emotional expressions which serve the social purpose of discerning and influencing the thoughts of others (e. and humans will infer social intentions on the basis of minimal cues, as Heider and Simmel (1944) demonstrated in their classic experiment involving chasing triangles and evading circles. Recent work has shown that children above age 4 and adults in disparate cultures (Germans and Amazonian Indians) can categorize intentions—chasing, fighting, following, playing, and courting (for adults)— from no more than the motion patterns of computerized v-shaped arrowheads (Barrett, Todd, Miller, & Blythe, in press). Most notably, humans have a deeply-felt need for social inclusion. Deprivation of social contact produces anxiety, loneliness, and depression (Baumeister & Leary, 1995); indeed, as William James (1890) observed: " Solitary confinement is by many regarded as a mode of torture Accuracy and Bias-3 too cruel and unnatural for civilised countries to adopt. " Participants in laboratory studies who are left out of a face-to-face …
Pre-Prosthetic Orthodontic Implant for Management of Congenitally Unerupted Lateral Incisors – A Case Report
(2016)

The maxillary lateral incisor is one of the most common congenitally missing teeth of the permanent dentition. With the advent of implants in the field of restorative dentistry, a stable and predictable fixed prosthetic replacement has become a reality, especially for young adult patients who suffer from congenital absence of teeth. The dual goals of establishment of functional stability as well as enhancement of esthetic outcomes are made achievable by the placement of implants. A multidisciplinary team approach involving the triad of orthodontist, periodontist and restorative dentist will ensure the successful completion of the integrated treatment approach in these patients. The present case report achieved successful implant based oral rehabilitation in a patient diagnosed with congenital absence of bilateral maxillary lateral incisors utilizing a preprosthetic orthodontic implant site preparation for the purpose of space gain.
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space
(2010)

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results.
 Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders of magnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results.
 As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial reduction in the space and running time. In our experiments, our technique was faster: (i) than distance browsing (a well-known method for solving the problem exactly) by several orders of magnitude, and (ii) than D-shift (an approximate approach with theoretical guarantees in low-dimensional space) by one order of magnitude, and at the same time, outputs better results.
Adversarial Examples for Generative Models
(2018)

We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.
Exploring hacker assets in underground forums
(2015)

Many large companies today face the risk of data breaches via malicious software, compromising their business. These types of attacks are usually executed using hacker assets. Researching hacker assets within underground communities can help identify the tools which may be used in a cyberattack, provide knowledge on how to implement and use such assets and assist in organizing tools in a manner conducive to ethical reuse and education. This study aims to understand the functions and characteristics of assets in hacker forums by applying classification and topic modeling techniques. This research contributes to hacker literature by gaining a deeper understanding of hacker assets in well-known forums and organizing them in a fashion conducive to educational reuse. Additionally, companies can apply our framework to forums of their choosing to extract their assets and appropriate functions.
Analysis of circulating tumor DNA to monitor metastatic breast cancer.
(2013)

BACKGROUND
The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer.


METHODS
We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points.


RESULTS
Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%).


CONCLUSIONS
This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation
(2017)

This paper addresses the problem of weakly supervised semantic image segmentation. Our goal is to label every pixel in a new image, given only image-level object labels associated with training images. Our problem statement differs from common semantic segmentation, where pixel-wise annotations are typically assumed available in training. We specify a novel deep architecture which fuses three distinct computation processes toward semantic segmentation &#x2013; namely, (i) the bottom-up computation of neural activations in a CNN for the image-level prediction of object classes, (ii) the top-down estimation of conditional likelihoods of the CNNs activations given the predicted objects, resulting in probabilistic attention maps per object class, and (iii) the lateral attention-message passing from neighboring neurons at the same CNN layer. The fusion of (i)-(iii) is realized via a conditional random field as recurrent network aimed at generating a smooth and boundary-preserving segmentation. Unlike existing work, we formulate a unified end-to-end learning of all components of our deep architecture. Evaluation on the benchmark PASCAL VOC 2012 dataset demonstrates that we outperform reasonable weakly supervised baselines and state-of-the-art approaches.
Skipping Refinement
(2015)

We introduce skipping refinement, a new notion of correctness for reasoning about optimized reactive systems. Reasoning about reactive systems using refinement involves defining an abstract, highlevel specification system and a concrete, low-level implementation system. One then shows that every behavior allowed by the implementation is also allowed by the specification. Due to the difference in abstraction levels, it is often the case that the implementation requires many steps to match one step of the specification, hence, it is quite useful for refinement to directly account for stuttering. Some optimized implementations, however, can actually take multiple specification steps at once. For example, a memory controller can buffer the commands to the memory and at a later time simultaneously update multiple memory locations, thereby skipping several observable states of the abstract specification, which only updates one memory location at a time. We introduce skipping simulation refinement and provide a sound and complete characterization consisting of “local” proof rules that are amenable to mechanization and automated verification. We present case studies that highlight the applicability of skipping refinement: a JVM-inspired stack machine, a simple memory controller and a scalar to vector compiler transformation. Our experimental results demonstrate that current model-checking and automated theorem proving tools have difficultly automatically analyzing these systems using existing notions of correctness, but they can analyze the systems if we use skipping refinement.
Fossil chrysophycean cyst flora of Racze Lake, Wolin Island (Poland) in relation to paleoenvironmental conditions
(1987)

The study presents stratigraphic distribution of fossil chrysophycean cysts in the bottom sediments of Racze Lake (Poland). Thirty morphotypes are described, most of them for the first time. The description of the cyst includes SEM microphotographs. The long-term relationship between the lake's conditions and the occurrence of characteristic morphotypes of chrysophycean cysts is discussed.
Design and implementation of a 120A resonant inverter for induction furnace
(2013)

This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.
Sharpness Enhancement and Super-Resolution of Around-View Monitor Images
(2018)

In the wide-angle (WA) images embedded in an around-view monitor system, the subject(s) in the peripheral region is normally small and has little information. Furthermore, since the outer region suffers from the non-uniform blur phenomenon and artifact caused by the inherent optical characteristic of WA lenses, its visual quality tends to deteriorate. According to our experiments, conventional image enhancement techniques rarely improve the degraded visual quality of the outer region of WA images. In order to solve the above-mentioned problem, this paper proposes a joint sharpness enhancement (SE) and super-resolution (SR) algorithm which can improve the sharpness and resolution of WA images together. The proposed SE algorithm improves the sharpness of the deteriorated WA images by exploiting self-similarity. Also, the proposed SR algorithm generates super-resolved images by using high-resolution information which is classified according to the extended local binary pattern-based classifier and learned on a pattern basis. Experimental results show that the proposed scheme effectively improves the sharpness and resolution of the input deteriorated WA images. Even in terms of quantitative metrics such as just noticeable blur, structural similarity, and peak signal-to-noise ratio. Finally, the proposed scheme guarantees real-time processing such that it achieves 720p video at 29 Hz on a low cost GPU platform.
Understanding the Potential of Interpreter-based Optimizations for Python
(2010)

The increasing popularity of scripting languages as general purpose programming environments calls for more efficient execution. Most of these languages, such as Python, Ruby, PHP, and JavaScript are interpreted. Interpretation is a natural implementation given the dynamic nature of these languages and interpreter portability has facilitated wide-spread use. In this work, we analyze the performance of CPython, a commonly used Python interpreter, to identify major sources of overhead. Based on our findings, we investigate the efficiency of a number of optimizations and explore the design options and trade-offs involved.
A Fast Parallel Algorithm for Thinning Digital Patterns
(1984)

A fast parallel thinning algorithm is proposed in this paper. It consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deleting the north-west boundary points and the south-east corner points. End points and pixel connectivity are preserved. Each pattern is thinned down to a "skeleton" of unitary thickness. Experimental results show that this method is very effective.
Lifetime study for a poly fuse in a 0.35 /spl mu/m polycide CMOS process
(2005)

Poly fuses are used as the base element for one time programmable cells in a standard CMOS process. Using a defined programming current, the resistance of the poly fuse increases irreversibly over several orders of magnitude. The goal of this study is to show that a poly fuse has a sufficient life time stability to be used as a storage element even in high reliability circuits. This paper shows the drift of the resistance of a poly fuse over the whole range of programming currents for a standard polycide 0.35 /spl mu/m CMOS process. The poly fuse for the selected process is build using two different layers, which gives a special performance in terms of programming current.
The relative success of alternative approaches to strategic information systems planning: an empirical analysis
(1999)

Strategic information systems planning (SISP) is an exercise or ongoing activity that enables organisations to develop priorities for information systems development. It has been suggested that the ‘SISP approach’, a combination of method, process and implementation, is the most complete way of describing SISP activity. Based upon questionnaire responses from 267 IT Directors, four distinct approaches to SISP have been derived using cluster analysis. A comparison of these four approaches with five approaches of Earl, M.J., 1993. Experiences in SISP, MIS Quarterly, (March), 1–24, indicates that three bear strong similarities to the ‘organisational’, ‘business-led’, and ‘administrative’ approaches, whilst the fourth cluster is related to both Earl’s ‘method-driven’ and ‘technological’ approaches. An analysis of the relationship between SISP approach and SISP success demonstrates that the ‘organisational approach’ is significantly more successful than the other three approaches. q 1999 Elsevier Science B.V. All rights reserved.